A Deep Neural Network Potential Study on the Mechanical and Thermophysical Properties of β‑HMX Molecular Crystal

Neural network potentials (NNPs) provide an efficient and accurate approach for predicting the mechanical and thermophysical properties of high-energy-density materials (HEDMs) under extreme temperature and pressure conditions, where experimental characterization is often limited and first-principles simulations become computationally demanding. In this work, an NNP for HEDMs is developed within a new active-learning framework that automatically eliminates structurally redundant configurations. The resulting NNP predicts the mechanical and thermophysical properties of β-cyclotetramethylene-tetranitramine (β-HMX) with density functional theory (DFT)-level accuracy while achieving orders-of-magnitude improvements in computational efficiency. The predicted bulk modulus of β-HMX at zero pressure agrees with experimental measurements within 13%, representing a substantial improvement over the classical molecular dynamics. Likewise, the volumetric thermal expansion coefficient α, constant-pressure heat capacity C _p, and constant-volume heat capacity C _v are all reproduced with deviations below 1%, demonstrating quantitative agreement with experiments. Beyond reproducing known benchmarks, the NNP enables efficient mapping of the coupled pressure-temperature dependence of thermophysical properties. Specifically, α decreases with pressure in two distinct regimes: a rapid drop at 0-6 GPa, which correlates with densification and the suppression of low-frequency phonon modes, followed by a more gradual decline at higher pressures where phonon stiffening becomes more uniform. Overall, this work addresses an important gap in the characterization of temperature- and pressure-dependent mechanical and thermophysical properties of HEDMs. The proposed training strategy provides a transferable framework for modeling the thermomechanical responses under extreme conditions.